Comparison of Non-parametric Bayesian Mixture Models for Syllable Clustering and Zero-Resource Speech Processing

Shreyas Seshadri, Ulpu Remes, Okko Räsänen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Zero-resource speech processing (ZS) systems aim to learn structural representations of speech without access to labeled data. A starting point for these systems is the extraction of syllable tokens utilizing the rhythmic structure of a speech signal. Several recent ZS systems have therefore focused on clustering such syllable tokens into linguistically meaningful units. These systems have so far used heuristically set number of clusters, which can, however, be highly dataset dependent and cannot be optimized in actual unsupervised settings. This paper focuses on improving the flexibility of ZS systems using Bayesian non-parametric (BNP) mixture models that are capable of simultaneously learning the cluster models as well as their number based on the properties of the dataset. We also compare different model design choices, namely priors over the weights and the cluster component models, as the impact of these choices is rarely reported in the previous studies. Experiments are conducted using conversational speech from several languages. The models are first evaluated in a separate syllable clustering task and then as a part of a full ZS system in order to examine the potential of BNP methods and illuminate the relative importance of different model design choices.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech Communication Association
Number of pages5
ISBN (Print)978-1-5108-4876-4
Publication statusPublished - Aug 2017
MoE publication typeA4 Article in a conference publication
EventInterspeech - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017
Conference number: 18

Publication series

NameInterspeech: Annual Conference of the International Speech Communication Association
ISSN (Electronic)1990-9772


Internet address


  • Non-parametric clustering
  • zero-resource processing
  • variational inference
  • Pitman-Yor process
  • von Mises-Fisher mixtures

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